update README.md
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@@ -65,7 +65,7 @@ The dataset used in LightRAG can be download from [TommyChien/UltraDomain](https
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### Generate Query
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### Generate Query
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LightRAG uses the following prompt to generate high-level queries, with the corresponding code located in `example/generate_query.py`.
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LightRAG uses the following prompt to generate high-level queries, with the corresponding code located in `example/generate_query.py`.
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```json
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```python
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Given the following description of a dataset:
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Given the following description of a dataset:
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{description}
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{description}
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### Batch Eval
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### Batch Eval
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To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
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To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
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```json
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```python
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---Role---
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---Role---
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You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
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You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
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---Goal---
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---Goal---
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## Code Structure
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## Code Structure
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```json
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```python
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.
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.
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├── examples
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├── examples
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│ ├── batch_eval.py
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│ ├── batch_eval.py
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```
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```
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## Citation
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## Citation
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```json
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```python
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@article{guo2024lightrag,
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@article{guo2024lightrag,
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title={LightRAG: Simple and Fast Retrieval-Augmented Generation},
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title={LightRAG: Simple and Fast Retrieval-Augmented Generation},
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author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
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author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
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primaryClass={cs.IR}
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primaryClass={cs.IR}
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}
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}
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```
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```
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